


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE     :					str      =       {
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       [
     "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MCTCTForCTC",
     "MCTCTModel",
     "MCTCTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=7     ,   __snake_case=3     ,   __snake_case=3_0     ,   __snake_case=4_0_0     ,   __snake_case=True     ,   __snake_case=None     ,   __snake_case=True     ,   __snake_case=1 / 2_5_5     ,   __snake_case=True     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=True     ,   ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         snake_case											=							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							num_channels
         snake_case											=							min_resolution
         snake_case											=							max_resolution
         snake_case											=							do_resize
         snake_case											=							size
         snake_case											=							do_rescale
         snake_case											=							rescale_factor
         snake_case											=							do_normalize
         snake_case											=							image_mean
         snake_case											=							image_std
         snake_case											=							do_pad
    def 						a_     (			self       ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         if not batched:
              snake_case											=							image_inputs[0]
              if isinstance(__snake_case     ,   Image.Image       ):
                   snake_case					,    snake_case											=							image.size
              else:
                   snake_case					,    snake_case											=							image.shape[1], image.shape[2]
              if w < h:
                   snake_case											=							int(self.size['''shortest_edge'''] * h / w       )
                   snake_case											=							self.size['''shortest_edge''']
              elif w > h:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							int(self.size['''shortest_edge'''] * w / h       )
              else:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							self.size['''shortest_edge''']

         else:
              snake_case											=							[]
              for image in image_inputs:
                   snake_case					,    snake_case											=							self.get_expected_values([image]       )
                   expected_values.append((expected_height, expected_width)       )
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[0]       )[0]
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						DetrImageProcessor if is_vision_available() else None
    def 						a_     (			self       ):
         snake_case											=							DetrImageProcessingTester(self       )
    @property
    def 						a_     (			self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_mean'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_std'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_normalize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_rescale'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''rescale_factor'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_resize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''size'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_pad'''       )       )
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )

         snake_case											=							self.image_processing_class.from_dict(
             self.image_processor_dict     ,   size=4_2     ,   max_size=8_4     ,   pad_and_return_pixel_mask=__snake_case       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   Image.Image       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   numpify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   np.ndarray       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   torch.Tensor       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    @slow
    def 						a_     (			self       ):
         # prepare image and target
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




    @slow
    def 						a_     (			self       ):
         # prepare image, target and masks_path
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

         snake_case											=							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   masks_path=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify masks
         snake_case											=							8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,   __snake_case       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




